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Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation
This study examines the status of nonmodal phonation (e.g. breathy and creaky voice) in British English using smartphone recordings from over 2,500 speakers. With this novel data collection method, it uncovers effects that have not been reported in past work, such as a relationship between speakers’...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861257/ https://www.ncbi.nlm.nih.gov/pubmed/33733211 http://dx.doi.org/10.3389/frai.2020.565682 |
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author | Gittelson, Ben Leemann, Adrian Tomaschek, Fabian |
author_facet | Gittelson, Ben Leemann, Adrian Tomaschek, Fabian |
author_sort | Gittelson, Ben |
collection | PubMed |
description | This study examines the status of nonmodal phonation (e.g. breathy and creaky voice) in British English using smartphone recordings from over 2,500 speakers. With this novel data collection method, it uncovers effects that have not been reported in past work, such as a relationship between speakers’ education and their production of nonmodal phonation. The results also confirm that previous findings on nonmodal phonation, including the greater use of creaky voice by male speakers than female speakers, extend to a much larger and more diverse sample than has been considered previously. This confirmation supports the validity of using crowd-sourced data for phonetic analyses. The acoustic correlates that were examined include fundamental frequency, H1*-H2*, cepstral peak prominence, and harmonic-to-noise ratio. |
format | Online Article Text |
id | pubmed-7861257 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78612572021-03-16 Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation Gittelson, Ben Leemann, Adrian Tomaschek, Fabian Front Artif Intell Artificial Intelligence This study examines the status of nonmodal phonation (e.g. breathy and creaky voice) in British English using smartphone recordings from over 2,500 speakers. With this novel data collection method, it uncovers effects that have not been reported in past work, such as a relationship between speakers’ education and their production of nonmodal phonation. The results also confirm that previous findings on nonmodal phonation, including the greater use of creaky voice by male speakers than female speakers, extend to a much larger and more diverse sample than has been considered previously. This confirmation supports the validity of using crowd-sourced data for phonetic analyses. The acoustic correlates that were examined include fundamental frequency, H1*-H2*, cepstral peak prominence, and harmonic-to-noise ratio. Frontiers Media S.A. 2021-01-25 /pmc/articles/PMC7861257/ /pubmed/33733211 http://dx.doi.org/10.3389/frai.2020.565682 Text en Copyright © 2021 Gittelson, Leemann and Tomaschek. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence Gittelson, Ben Leemann, Adrian Tomaschek, Fabian Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation |
title | Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation |
title_full | Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation |
title_fullStr | Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation |
title_full_unstemmed | Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation |
title_short | Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation |
title_sort | using crowd-sourced speech data to study socially constrained variation in nonmodal phonation |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861257/ https://www.ncbi.nlm.nih.gov/pubmed/33733211 http://dx.doi.org/10.3389/frai.2020.565682 |
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